Discovery and design of soft polymeric bio-inspired materials with multiscale simulations and artificial intelligence

Abstract

Establishing the “Materials 4.0” paradigm requires intimate knowledge of the virtual space in materials design.

Data‐Driven Approaches Toward Smarter Additive Manufacturing

“…The core of atomistic modeling lies in accurate and efficient techniques for numerically solving the Schrödinger equation for complex systems that consist of multiple atoms. [20,28] Examples of using DFT in materials science, and more specifically in materials design, include studying the formation of defects in metallic materials, [29][30][31] tailoring mechanical properties of alloys, [32][33][34] and polymeric materials design. [20,35,36] Using quantum mechanical calculation such as DFT to directly inform material research for AM is shown as the blue flow in Figure 2.…”

“…[20,28] Examples of using DFT in materials science, and more specifically in materials design, include studying the formation of defects in metallic materials, [29][30][31] tailoring mechanical properties of alloys, [32][33][34] and polymeric materials design. [20,35,36] Using quantum mechanical calculation such as DFT to directly inform material research for AM is shown as the blue flow in Figure 2. For instance, in powder bed fusion (PBF) AM, the surface energies of different material phases drive the melt penetration phenomenon and strongly affect the quality of the resulting component.…”

“…[53] Computational quantum mechanical methods are generally accurate at the molecular scale but become extremely computationally expensive at much larger spatiotemporal scales. [20] These costs limit computational quantum mechanical methods to exploring phenomena that occur on the order of tens of picoseconds and a few thousand atoms. In contrast, MD methods provide an alternative for overcoming these limitations.…”

“…This is because the central component of MD, the potential energy surfaces (PES), is usually fitted and applied empirically, which introduces approximation errors. [20] To alleviate this drawback, there is a growing interest in using ML methods to derive MD atomic potentials from large quantum mechanical datasets. This is done in two steps: first, transform the reference atomic configurations into structural descriptors and second, use ML algorithms to learn the mapping between the descriptors and the corresponding potential energy.…”

“…Interested readers can learn more about these techniques from relevant reviews. [19][20][21] We will focus on data-driven methods that are most applicable to AM of metallic products, while drawing parallels to developments in the field of polymer AM when such methods discussed are yet to be implemented in metallic AM. A detailed review is available elsewhere on the state-of-the-art techniques in data-driven AM that are more specific to polymeric materials.…”